{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Activation Maximization on MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets build the mnist model and train it for 5 epochs. It should get to about ~99% test accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 0s 0us/step\n",
      "x_train shape: (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n",
      "WARNING:tensorflow:From C:\\Users\\tkramer\\AppData\\Local\\Continuum\\miniconda3\\envs\\donkey\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From C:\\Users\\tkramer\\AppData\\Local\\Continuum\\miniconda3\\envs\\donkey\\lib\\site-packages\\tensorflow\\python\\keras\\layers\\core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "WARNING:tensorflow:From C:\\Users\\tkramer\\AppData\\Local\\Continuum\\miniconda3\\envs\\donkey\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 6s 105us/sample - loss: 0.2484 - acc: 0.9249 - val_loss: 0.0524 - val_acc: 0.9836\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 4s 63us/sample - loss: 0.0859 - acc: 0.9748 - val_loss: 0.0341 - val_acc: 0.9880\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 4s 62us/sample - loss: 0.0652 - acc: 0.9801 - val_loss: 0.0323 - val_acc: 0.9901\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 4s 63us/sample - loss: 0.0518 - acc: 0.9839 - val_loss: 0.0281 - val_acc: 0.9905\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 4s 64us/sample - loss: 0.0477 - acc: 0.9849 - val_loss: 0.0282 - val_acc: 0.9915\n",
      "Test loss: 0.02815446266430663\n",
      "Test accuracy: 0.9915\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow.python.keras as keras\n",
    "\n",
    "from tensorflow.python.keras.datasets import mnist\n",
    "from tensorflow.python.keras.models import Sequential, Model\n",
    "from tensorflow.python.keras.layers import Dense, Dropout, Flatten, Activation, Input\n",
    "from tensorflow.python.keras.layers import Conv2D, MaxPooling2D\n",
    "from tensorflow.python.keras import backend as K\n",
    "\n",
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 5\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "# the data, shuffled and split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
    "    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
    "    input_shape = (1, img_rows, img_cols)\n",
    "else:\n",
    "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(3, 3),\n",
    "                 activation='relu',\n",
    "                 input_shape=input_shape))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes, activation='softmax', name='preds'))\n",
    "\n",
    "model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.Adam(),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train, y_train,\n",
    "          batch_size=batch_size,\n",
    "          epochs=epochs,\n",
    "          verbose=1,\n",
    "          validation_data=(x_test, y_test))\n",
    "\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dense Layer Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualize activation over final dense layer outputs, we need to switch the `softmax` activation out for `linear` since gradient of output node will depend on all the other node activations. Doing this in keras is tricky, so we provide `utils.apply_modifications` to modify network parameters and rebuild the graph.\n",
    "\n",
    "If this swapping is not done, the results might be suboptimal. We will start by swapping out 'softmax' for 'linear' and compare what happens if we dont do this at the end."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets start by visualizing input that maximizes the output of node 0. Hopefully this looks like a 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e194c8b2b0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from vis.visualization import visualize_activation\n",
    "from vis.utils import utils\n",
    "from tensorflow.python.keras import activations\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (18, 6)\n",
    "\n",
    "# Utility to search for layer index by name. \n",
    "# Alternatively we can specify this as -1 since it corresponds to the last layer.\n",
    "layer_idx = utils.find_layer_idx(model, 'preds')\n",
    "\n",
    "# Swap softmax with linear\n",
    "model.layers[layer_idx].activation = activations.linear\n",
    "model = utils.apply_modifications(model)\n",
    "\n",
    "# This is the output node we want to maximize.\n",
    "filter_idx = 0\n",
    "img = visualize_activation(model, layer_idx, filter_indices=filter_idx)\n",
    "plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Hmm, it sort of looks like a 0, but not as clear as we hoped for. Activation maximization is notorious because regularization parameters needs to be tuned depending on the problem. Lets enumerate all the possible reasons why this didn't work very well.\n",
    "    \n",
    "- The input to network is preprocessed to range (0, 1). We should specify `input_range = (0., 1.)` to constrain the input to this range.\n",
    "- The regularization parameter default weights might be dominating activation maximization loss weight. One way to debug this is to use `verbose=True` and examine individual loss values.\n",
    "\n",
    "Lets do these step by step and see if we can improve it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Debugging step 1: Specifying input_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e1c1b454a8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = visualize_activation(model, layer_idx, filter_indices=filter_idx, input_range=(0., 1.))\n",
    "plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better but still seems noisy. Lets examining the losses with `verbose=True` and tuning the weights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Debugging step 2: Tuning regularization weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the issues with activation maximization is that the input can go out of the training distribution space. Total variation and L-p norm are used to provide some hardcoded image priors for natural images. For example, Total variation ensures that images are blobber and not scattered. Unfotunately, sometimes these losses can dominate the main `ActivationMaximization` loss.\n",
    "\n",
    "Lets see what individual losses are, with `verbose=True`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e1d9d1d3c8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AsGVzK5LHSM6RnOss6v5ZxpidY9tiJ7kXwHcAfCoirm53u4g4HhGzETHbnNbN8IwxO8e2xE5yAhtC/2ZEfLf38DmSR3rjRwCc35kQjTGDILXeSBLA1wCciogvbBp6AsADAB7pff9+Ohvr2Wshy1DX82nWEutNlZK+uKI/sSys6HTIzKbJbMUQZZHbh7QtyKRcc3OlXtvkprDesnMhS/XMbEHhQqGxpifvJp4k1c6Rxy7tteRUTq25Crbjs98J4GMAXiB5ovfYZ7Eh8m+T/DiAVwF8pK8IjDFDIRV7RPwQ1deWDww2HGPMTuHbZY0pBIvdmEKw2I0pBIvdmEKw2I0phOGmuEaSdphkFUpPN/M9l/XO11vaV202q5cqK0PdEB49AGCv9sIj8YQV3KWN8OzuhG5yUFQZawDoqFrViV3cnUh89BotvrP7Mpi0+M5oJKWqs9RiRb9l031lN6YQLHZjCsFiN6YQLHZjCsFiN6YQLHZjCsFiN6YQhl5KWnmfjaT7r7J8W0l7305SzrmbeNnLB6v3P9HUXvbBKd03ed/uVTl+4fI+Ob5+dbJ6sJG0PZ7Si95N/Ohu4ifL1sRZDYLkUlSn5DKTds9I8vSzewSYnMvKx+8mPnq/9wD4ym5MIVjsxhSCxW5MIVjsxhSCxW5MIVjsxhSCxW5MIQzfZxcWYpbPrnKAM+cxq3/eTTzbuFhd+/1K0t53ed+EHG8lufSddvKeLGLP8vhjsl7eNpLYQ11PspzuLG07TcavHspr9e9svrvetx6XkYlBX9mNKQSL3ZhCsNiNKQSL3ZhCsNiNKQSL3ZhCsNiNKYTt9Gc/CuAbAG7ChnN5PCK+RPJhAH8B4ELvqZ+NiCf1zrSXnlib6Aq7OvMmMyO+uabHQ3jZXNc++mqSK7+a5XUnNfFVXnfaJzzz4WvmnMu5k9eV5pQnXrc6n9J1yQ5Jsi5pTXu1bdY/QZ3rYkm2c1NNG8CnI+InJPcBeI7kU72xL0bE325jH8aYEbOd/uxnAZzt/TxP8hSAm3c6MGPMYPm1PmyQfBeA9wH4Ue+hB0k+T/JRkocqtjlGco7kXGdxsVawxpj+2bbYSe4F8B0An4qIqwC+AuA9AG7HxpX/81ttFxHHI2I2Imab09MDCNkY0w/bEjvJCWwI/ZsR8V0AiIhzEdGJiC6ArwK4Y+fCNMbUJRU7SQL4GoBTEfGFTY8f2fS0DwM4OfjwjDGDYjv/jb8TwMcAvEDyRO+xzwK4n+Tt2EiqOw3gE+meQtsG3SwalS2ZtPfNSgfXsTuYOEiNFf2e2t2lfcM0nbLO3RJZpmaWCZqVc1YvrWaaaGaPyRTXGi2TgW2kY2fWnbKgE8sxnbuC7fw3/ofY+pBrT90YM1b4DjpjCsFiN6YQLHZjCsFiN6YQLHZjCsFiN6YQhltKmjr1L/NNO6KVbZqSmLXBTUoLy7hrptfWRs2vs28RiSGc+uiJJyzvEUjWLS2pnPjN8pgnxzubO40tazctts9e160P/Uvl2KWozj/xld2YQrDYjSkEi92YQrDYjSkEi92YQrDYjSkEi92YQmBElhQ8wMnICwD+a9ND1wN4c2gB/HqMa2zjGhfg2PplkLG9MyJu2GpgqGL/lcnJuYiYHVkAgnGNbVzjAhxbvwwrNn+MN6YQLHZjCmHUYj8+4vkV4xrbuMYFOLZ+GUpsI/2b3RgzPEZ9ZTfGDAmL3ZhCGInYSd5N8mckXyb5mVHEUAXJ0yRfIHmC5NyIY3mU5HmSJzc9NkPyKZIv9b5v2WNvRLE9TPL13tqdIHnPiGI7SvIZkqdIvkjyod7jI107EddQ1m3of7OTbAL4DwB/AuAMgB8DuD8i/n2ogVRA8jSA2YgY+Q0YJP8QwAKAb0TE7/Ye+xsAlyLikd4b5aGI+Ksxie1hAAujbuPd61Z0ZHObcQD3AfhzjHDtRFx/iiGs2yiu7HcAeDkiXomINQDfAnDvCOIYeyLiWQCXrnn4XgCP9X5+DBsny9CpiG0siIizEfGT3s/zAN5uMz7StRNxDYVRiP1mAK9t+v0MxqvfewD4AcnnSB4bdTBbcDgizgIbJw+AG0ccz7WkbbyHyTVtxsdm7fppf16XUYh9q+Jf4+T/3RkRvwfgQwA+2fu4arbHttp4D4st2oyPBf22P6/LKMR+BsDRTb+/A8AbI4hjSyLijd738wC+h/FrRX3u7Q66ve/nRxzP/zBObby3ajOOMVi7UbY/H4XYfwzgVpLvJjkJ4KMAnhhBHL8CyeneP05AchrABzF+raifAPBA7+cHAHx/hLH8EuPSxruqzThGvHYjb38eEUP/AnAPNv4j/58A/noUMVTE9ZsA/q339eKoYwPwODY+1q1j4xPRxwFcB+BpAC/1vs+MUWx/D+AFAM9jQ1hHRhTbH2DjT8PnAZzofd0z6rUTcQ1l3Xy7rDGF4DvojCkEi92YQrDYjSkEi92YQrDYjSkEi92YQrDYjSmE/wZC+9ZcbSTvGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = visualize_activation(model, layer_idx, filter_indices=filter_idx, input_range=(0., 1.), verbose=False)\n",
    "plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, `ActivationMax Loss` is not bouncing bouncing around and converging? Perhaps we could get that loss to be lower by reducing weights of other losses that might be dominating the overall loss being minimized. \n",
    "\n",
    "The simplest way to tune these weights is to first start with `0.` weights for all regularization losses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e1e3033208>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = visualize_activation(model, layer_idx, filter_indices=filter_idx, input_range=(0., 1.), \n",
    "                           tv_weight=0., lp_norm_weight=0., verbose=False)\n",
    "plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does indeed go to much lower values, but the image looks less natural. Let's try varous range of total variation weights to enforce naturalness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for tv_weight in [1e-3, 1e-2, 1e-1, 1, 10]:\n",
    "    # Lets turn off verbose output this time to avoid clutter and just see the output.\n",
    "    img = visualize_activation(model, layer_idx, filter_indices=filter_idx, input_range=(0., 1.), \n",
    "                               tv_weight=tv_weight, lp_norm_weight=0.)\n",
    "    plt.figure()\n",
    "    plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see how total variation loss is enforcing blobbiness. In this case the default value of `tv_weight=10` seems to work very well. The point of this exercise was to show how weights can be tuned.\n",
    "\n",
    "Lets visualize all other output categories and see what we get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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J2cbiwcXpWJw9lvzsm3uOhfsvjUVy6ft83wqRZwQqlcjDEb3OsxF9ke0d6zuSq1+IPaDgtS1H1t1Avjrgx9K9uRMAoDjtPPPh+BU9spO8ieQJkvvqlq0heSfJ/fn/1bF+hBCtZSGn8TcDuHTOsk8CuMvMzgNwV/5eCNHGRMVuZvcAGJqz+HIAt+SvbwHwriX2SwixxCz2Bt1GMzsKAPn/DaEPktxFci/JveXp2APDQojlYtnvxpvZHjPbaWY7S12RpAkhxLKxWLEfJ7kZAPL/J5bOJSHEcrBYsd8B4Or89dUAbl8ad4QQy0U0zk7ySwAuAbCO5GEAnwJwPYDbSH4QwDMA3rPQFXox6d4TfnJ178mwrVaKzAPe01hOecWJw3dEYtGxGH4xXEY8s0fi9KXxcHC1Y8oPCE+vivi+xa9rf86WQdd++LlVQVttMDLoscnXi7694tRgLw756y6NNjYPQGyb1xzlxfLZyyvCvnn9RsVuZlcGTG+NtRVCtA96XFaIRJDYhUgEiV2IRJDYhUgEiV2IRGjuVNIAwHDYIBqi8lL7qn4YpjOa6un/7nmhvXK4MjCABaTATkTKKg/77buGw9+tUPG/9+QmPw30oxd9x195hD2n3xQ2RsJXsanFESm7XHsuPMV2z1BkKugJf9WFWOjNr/jsKo+RzNxKr7NNna+lI7sQiSCxC5EIErsQiSCxC5EIErsQiSCxC5EIErsQidDUOHutCEw5KZWx6XsLTjw7lqIam/o31r7aHfbNS38FEI0Xx+Lovcf9oG6hEraNbvPnND771Ydc+4dXPeXa7/MzYHFr18VB23AkFh3LcEVkqumOifCxzCJ7fqycdMeUb/e2CQAUI+09eo87/Trp0jqyC5EIErsQiSCxC5EIErsQiSCxC5EIErsQiSCxC5EITY2zWwGo9MaSlMN0TMYCr2FqRX+9sRK7sZx0j45J39495Ccwx2K20yvDzg1f6Nf//eCmh1x7if7AHCz7BXxLTtnkWqe/PWPfm5HnMswxl/v8dcf20pnwDNkAgI7IHAVevnysZHOlsLjy4TqyC5EIErsQiSCxC5EIErsQiSCxC5EIErsQiSCxC5EIzY2zF4Fyf9geK4Przd0ei2UXypG4aiS2CScUXpiJxFQjzwfE5gkvR55NGDk7bN9+ll9S+cxOpw42gMHquL/u2hbXXq4u/ngSG9dYnN0b12q3v01srV9He9Vqf1xinBpcEbR1REpZ03n+wJuXIbolSN5E8gTJfXXLdpN8luQD+d9lsX6EEK1lIT+7NwO4dJ7lf2FmO/K/by2tW0KIpSYqdjO7B8BQE3wRQiwjjdyg+wjJh/LT/OAD0iR3kdxLcm91orHrHCHE4lms2D8D4CUAdgA4CuDPQx80sz1mttPMdhZ7+xa5OiFEoyxK7GZ23MyqZlYD8FkA4SlEhRBtwaLETnJz3dtfB7Av9FkhRHsQjbOT/BKASwCsI3kYwKcAXEJyB7LZ2A8C+O0Fr9EJb86sjOSrO2HVWP5waXTxMVnAfwYgFkeP5WWXe3zfZpy59gFgZk3YuZqX1A3gZGXAt1f9OP0z02td+8RUV9hY9Met6jQFAFYj9dm7nP4ju5pN+tIYKTlFDBbCVPg42zHewL7q2KJiN7Mr51l8Y6ydEKK90OOyQiSCxC5EIkjsQiSCxC5EIkjsQiRCU1NcaX4YKprq6YSYIhEkTK/zwxlFp7wvAHQNeaWm/XUXI2WNY/MW1yLTXHshqPFpvy7y0bI/J/KB0hrfPr7OtZdnnF0ssr3j0zHHppIO20tj/rp7nvPtxsZCbx3T4difFSID44QNvVLSOrILkQgSuxCJILELkQgSuxCJILELkQgSuxCJILELkQjNjbNXga7TXm5hrFBuOOBcWRGJTUbomIylyDptndgmEC8HPTPgrzuW6unFXccn/MaPjW1y7ds6/ekHC/RzRYsd4Wcjqn3+3OGFQX/gugf9dXeOhe09J/yHIzpP+RuV1ViO7OLt1unLstYZHpfCTFgHOrILkQgSuxCJILELkQgSuxCJILELkQgSuxCJILELkQhNj7OXxsPxRT8GD1ScKZcr3f7vVmw659KEH6f32sfyzSc2+L5NbvK/d3mVH4+mU9q4POzH2U9OOjW0AUx5NYABrOn0S3pdtO1Q0DZW8X07tilc1hgAnnvcn8Z60w/CttKIX5K5MOZPQsCyv0NZKSKtmrO/RfP8w76zqji7EMkjsQuRCBK7EIkgsQuRCBK7EIkgsQuRCBK7EImwkJLN2wF8HsAmZBHAPWb2aZJrAHwFwFnIyjZfYWan3L5qfpydkRzgohNP7nnOb9sx4ceqrRgp/1sK2yfX+MNYjsxpXztj0rX3dPox3ckTvUFbwSkNvBBi88o/PrzRtZ8YC8fx1/X7MfpXrz/s2o/3j7j2f5k+J2hjNTxmANBX8PeHwpS/TTgdKyYQ3mc4E2nr4chgIXtCBcC1ZvZyAK8D8GGSFwD4JIC7zOw8AHfl74UQbUpU7GZ21Mzuz1+PAngUwFYAlwO4Jf/YLQDetVxOCiEa52c6xyN5FoBXA7gXwEYzOwpkPwgANiy1c0KIpWPBYifZD+BrAD5mZv7F0k+320VyL8m95elIgS0hxLKxILGTLCET+hfM7Ov54uMkN+f2zQBOzNfWzPaY2U4z21nq8pMuhBDLR1TsJAngRgCPmtkNdaY7AFydv74awO1L754QYqlYSIrrGwFcBeBhkg/ky64DcD2A20h+EMAzAN4T7YlAzVmjFfzfnpkV4XBI56gfevOm2AWAWslft5deG50qepW/7rM3+vWBB8f6XHtlOOKAw8lxv+8f4GzXvv+Rra69/2DYt5MVvxz0kxf601y/+6L7XHvtDeFt9vCa7W7blfv8s9CVB/3QW9dQJIV2xgkFR0LQrDj7kxMyjIrdzL6P8ITub421F0K0B3qCTohEkNiFSASJXYhEkNiFSASJXYhEkNiFSISmTiUNAwpOeHEyUrp4cn3YPrHJb9s53OnaizOR2KYT2qx2RUour/BjsuWqHyc/fcyfUnnlUWcqaT+Mjumyvwus7fbTUJ/o8p8h6DodPp6s2u+XRR54xp/G+vbhX3Dt5170TNC29Uz/2YYj0+tde7nfH7euU/427Rp2plQf8dOxvXTr6tPh8daRXYhEkNiFSASJXYhEkNiFSASJXYhEkNiFSASJXYhEaG6cPULnWKR0sROHr/phdEQqD6PoV+iFOT+L41t8vwc2+NNxdRUj0xJX/N9kb44A77kGABgf98smPzcVCdT7jxigYyo8NqVjw27b0oODrv2lT/j57iP3bgvaRrf7cfBef1hQiMz2bLGpqKvOPhMp2Vxwnglhg1NJCyFeBEjsQiSCxC5EIkjsQiSCxC5EIkjsQiSCxC5EIjQ1zk4zFKfCQURW/djkwMFwELHaGYlrVvxYuBdHB4ByX/gDXq47AExM+EHbE15wtEFKI37fffu6XfvTRyLzqz/rj3vvsfADDLHSxLWK//yB7T/o2lccCOezr9zklya0Xn9car2RBzsiFCbD352T/kMfXgzfKyWtI7sQiSCxC5EIErsQiSCxC5EIErsQiSCxC5EIErsQiRCNs5PcDuDzADYhy7TdY2afJrkbwIcAnMw/ep2Zfcvry0hUu5x5rcuRWLiTgtwxHam/XvTjwbFYuTnNu0/6fddGel37TNG3r5h0zSiNO/nNfqgahVP+mPeciK3bT5jvmAjHk2sr/Fz52JGoNjjkf6Aa9q3yk0P+urv8ZyNY8L1jjx+nRzm8Yazo59p73wvOswkLeaimAuBaM7uf5AoA95G8M7f9hZn92QL6EEK0mKjYzewogKP561GSjwLYutyOCSGWlp/pmp3kWQBeDeDefNFHSD5E8iaSqwNtdpHcS3JvedqfnkkIsXwsWOwk+wF8DcDHzGwEwGcAvATADmRH/j+fr52Z7TGznWa2s9TVvwQuCyEWw4LETrKETOhfMLOvA4CZHTezqpnVAHwWwMXL56YQolGiYidJADcCeNTMbqhbvrnuY78OYN/SuyeEWCoWcjf+jQCuAvAwyQfyZdcBuJLkDgAG4CCA317QGp0oVbk/8tvjzb7b4Ye/SpN+iKnqlMEFgIITwopNK1yaaDCFNRIWrHaHfa/5Ub3oVNO1ou97acIft0pfeA7vkjedMgCb8dNIC6tWuvbqYLgsMzv9vmvTfpppodcf2NqYX+qaHWHpWWTdru/OkC7kbvz3Mb9E3Zi6EKK90BN0QiSCxC5EIkjsQiSCxC5EIkjsQiSCxC5EIjS3ZDP9eHhsuudyj5Me2+BU0bHSw25Z5Mi6vdgnEC8nHcNL/a1GMi0rkWzKjvFI+m4kDl+YjgTyHaw7MjDDo67Zi0fXRiNtS34cvqFYeKQ9I+m1pW+GU4P5IUcjbq9CiBcNErsQiSCxC5EIErsQiSCxC5EIErsQiSCxC5EINFu+csEvWBl5EsBP6hatAzDYNAd+NtrVt3b1C5Bvi2UpfTvTzNbPZ2iq2F+wcnKvme1smQMO7epbu/oFyLfF0izfdBovRCJI7EIkQqvFvqfF6/doV9/a1S9Avi2WpvjW0mt2IUTzaPWRXQjRJCR2IRKhJWIneSnJx0k+SfKTrfAhBMmDJB8m+QDJvS325SaSJ0juq1u2huSdJPfn/+etsdci33aTfDYfuwdIXtYi37aT/H8kHyX5CMnfyZe3dOwcv5oybk2/ZidZBPAEgLcBOAzgRwCuNLN/aaojAUgeBLDTzFr+AAbJNwMYA/B5M7swX/ZfAQyZ2fX5D+VqM/u9NvFtN4CxVpfxzqsVba4vMw7gXQA+gBaOnePXFWjCuLXiyH4xgCfN7ICZzQD4MoDLW+BH22Nm9wAYmrP4cgC35K9vQbazNJ2Ab22BmR01s/vz16MAZsuMt3TsHL+aQivEvhXAobr3h9Fe9d4NwLdJ3kdyV6udmYeNZnYUyHYeABta7M9comW8m8mcMuNtM3aLKX/eKK0Q+3yTmrVT/O+NZvYaAL8C4MP56apYGAsq490s5ikz3hYstvx5o7RC7IcBbK97vw3AkRb4MS9mdiT/fwLAN9B+paiPz1bQzf+faLE/z9NOZbznKzOONhi7VpY/b4XYfwTgPJJnk+wE8D4Ad7TAjxdAsi+/cQKSfQDejvYrRX0HgKvz11cDuL2FvvwU7VLGO1RmHC0eu5aXPzezpv8BuAzZHfmnAPx+K3wI+HUOgAfzv0da7RuALyE7rSsjOyP6IIC1AO4CsD//v6aNfLsVwMMAHkImrM0t8u1NyC4NHwLwQP53WavHzvGrKeOmx2WFSAQ9QSdEIkjsQiSCxC5EIkjsQiSCxC5EIkjsQiSCxC5EIvx/rRxujVClPGkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YhSgEiV2IQpDYhSgEiV2IQpDYhSiE/w9MYvqMv2rY3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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C7PMV31pJ8b+XuvuLgdcCv1tdroqFsaBpvLvFPNOMrwgWO/15p/RC7PuArW3vzwHiqohdxN2fqP4fBL7EypuK+qlTM+hW/w/22J8fsZKm8Z5vmnFWwNj1cvrzXoj9duACM/s5MxsA3grc1AM/fgozG65unGBmw8CrWXlTUd8EXFO9vgb4cg99+QlWyjTeqWnG6fHY9Xz6c3fv+h9wBa078j8A/lUvfEj4dT5wV/V3X699Az5D67JuhtYV0TuAjcDNwMPV/w0ryLdPAvcAd9MS1pYe+fbLtH4a3g3cWf1d0euxC/zqyrjpcVkhCkFP0AlRCBK7EIUgsQtRCBK7EIUgsQtRCBK7EIUgsQtRCP8f2acXP0ob/ssAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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n0//zQv0IIRpLNZfxNwK4aNKyTwG4x8xOBXBP+l4I0cQExW5mDwCYfA17CYCb0tc3AXjXDPslhJhhpnuDbrGZ7QOA9P+irBVJriO5keTG8cHp/4YSQtTGrN+NN7MNZrbWzNbmu/0igkKI2WO6Yj9Asg8A0v/+bVMhRMOZrtjvBHBF+voKAN+dGXeEELNFMM5O8hYAFwJYQHI3gE8DuA7AN0l+CMBzAN47E85wJHDs8abLDhVYD9kDHXhzkJ/c1u+2PW2ZHw9+ymrLGd+5JzsW/vjJfm70pXM3ufZ7Tz7dtT+xp9O1lzqyx+2s//Cc2/ajy/xnBJ4e9ecB+MGBszNtN+8/z207PhQoJDDuP9eR7/HnCSg77c17ngQARh27029Q7GZ2WYbpzaG2QojmQY/LChEJErsQkSCxCxEJErsQkSCxCxEJ9U1xNYDF6ZWbBYDcyPTbhigfcabnBbB5T3Ya6emn+6G1y5f+zLX/qOMVrv0nT73ctRf2ZKfY3rrtXLftH5/nh97+aoX/CMXnCm9z7QeGs0sfv2nBk27bjSdOce3f2v5K196/P3vb+SH/PBeoAA4uGg2s4IdyrZSd4soTfkpzztOQk0KuM7sQkSCxCxEJErsQkSCxCxEJErsQkSCxCxEJErsQkVDnqaThxsPzXhwdfpZqy5DfloHqv3kvdgmgfDA7Zntzea3b9gNnbnTtL+s46tqZ82O2XXuyfR8em+u2/cyKN7j2v1r8oGu/ZskPXfu9Q2dm2rYNZ85mBgB4cPcq1z54wJ/5iE66Z7k9EAfvLLn2lkAcvTTqx8o57EwlHZj2fNwrda2SzUIIiV2ISJDYhYgEiV2ISJDYhYgEiV2ISJDYhYiEusbZWfZj6aHZnluGnVxdPyyKvF+ZOBiH954PGDvqly1+brjXtZ8351nX/vIlh1z7zsXLM22ji/wPtrrd7/t42W+/tZid5w8Am/uz6zJv2pPtNwCMDPilsNFSds25nmzfy6Hpmkv+cxelfn/+g9yw37+1Zu/tDMT42zuzy0XTGROd2YWIBIldiEiQ2IWIBIldiEiQ2IWIBIldiEiQ2IWIhLrG2Y2AOYeXXHb4MGnv5OpaIC5qfnoxSh2+fXhpduxzwfJjbtuh8UBMNpAb/e6+X7j2b12QPagv6/Zz5d/Q5c/dvjlQFvmOg/7c7U8/vzDTVhoLfClj/nea6wrknBey7eXAxPClnO+blfz25Q7/GQC0ZtvzgecHxsedbTulxYNndpI3kDxI8rGKZetJ7iG5Jf27ONSPEKKxVHMZfyOAi6ZY/gUzW5P+3TWzbgkhZpqg2M3sAQBH6uCLEGIWqeUG3UdJPpJe5s/LWonkOpIbSW4sDw7VsDkhRC1MV+z/AGA1gDUA9gH4XNaKZrbBzNaa2dpcd9c0NyeEqJVpid3MDpjZuJmVAXwZwPkz65YQYqaZlthJ9lW8fTeAx7LWFUI0B8E4O8lbAFwIYAHJ3QA+DeBCkmuQpKDvAPDhajfohAFhIW+csCoDYc0QpR4/1r18dXbe90ntw27bh57y64w/vWCBa/+z0+527e9bmj0vfdkbcABbRvx89J8N+LXhjxf9BxR6u05k2kZGC25btPjfSXnQb19szz6XdfT4Exx0dfr11/tz/ucO5cvnCtk7rDnz3QNAcTj7uQ2vbVDsZnbZFIuvD7UTQjQXelxWiEiQ2IWIBIldiEiQ2IWIBIldiEiob8lm80NkgSgRcl7bwGGrOMcP4+RWDbr2s3v3ZdrufvoMt23PY/6UyMdf5ccNV7UedO27xuZn2m47dK7btq/9uGtvCcyxXchN3z632w9ZHjrhh9a8kswAYE5Z5GKr33ebkx4LAG3tY669SL//spOSHUqf9aY192w6swsRCRK7EJEgsQsRCRK7EJEgsQsRCRK7EJEgsQsRCfWNs9OfDjoXmg7aOTSNd/hx9GKfHxc9ty87jg4ATx5flGlrebLTbTva6/v24XMecO3nt/kx2//6zIWZth37s2PwAPCRNfe79lwgd/in+/303Y5C9rgv6/Gn4A5NsX3o+R7Xbieyd+/SsL/rj7bVJg0L1B+3ojNVdUAHbm1z5+vSmV2ISJDYhYgEiV2ISJDYhYgEiV2ISJDYhYgEiV2ISKhvnB0Ay9kxxHKggm/OiS+GSjKftMDPV+8p+FMHb9mVPeUyAzH+RWsOuPaPzXvCtX+1v8+1b392cbYxMMV2W85//uCew36u/uFdJ7n2/Jzs/pes9HPpe9r87+RYmz+d86gTZw/hlkUG0Nrq57uPIjBNtrfLBMLs00VndiEiQWIXIhIkdiEiQWIXIhIkdiEiQWIXIhIkdiEioZqSzcsBfBXAyUiithvM7IskewF8A8BKJGWbLzWzo25nBj++6AXSAZSzK9Wi3Oa3ZSA3+tl+P+973IvZLi66bV+3eLtr3zbmx2y/fWCta8/3Zz9kkF+WXTIZAMZCDyiEcOYnAID2juyxKY772w6Vm87n/YcIcp1ejW+3KVpa/L6LRV865dHAuHpz3ofi7NOMw1dzZi8BuNrMzgTwGgAfIXkWgE8BuMfMTgVwT/peCNGkBMVuZvvMbHP6egDA4wCWArgEwE3pajcBeNdsOSmEqJ2X9Jud5EoArwTwEIDFZrYPSA4IALLnbRJCNJyqxU6yG8BtAD5uZv0vod06khtJbiwPDU3HRyHEDFCV2EkWkAj9ZjO7PV18gGRfau8DMGX1QTPbYGZrzWxtrqtrJnwWQkyDoNhJEsD1AB43s89XmO4EcEX6+goA351594QQM0U1OYCvBXA5gEdJbkmXXQvgOgDfJPkhAM8BeG+wpxxQbvXyVP3m5U4nHNLmlw4Ohd76R/yyymzN3nahLZDuWPaH+Rmn5DIAPHPYt+dGs2MxpTE/BFQIlGQ+o8dPzz2+yk8zPTGWneo5MNbuth0p+ePWEgi9tXVkp9eGQmehFNexQHuOBEJvbrp2QAie3QnLBcVuZj9xunhzqL0QojnQE3RCRILELkQkSOxCRILELkQkSOxCRILELkQk1HcqaQPolKN1Y/Dw27Lbj7m2tvjx5FIg3dKLpYdSLYuBOHs7/emcT5l/xLVvXZhdMrolkDa8fXiha+8v+XH0ECdGs/OSiyV/zC2U4poLpLgWsj976DsrBXwLtnet/r4cmv7b/UYdo87sQkSCxC5EJEjsQkSCxC5EJEjsQkSCxC5EJEjsQkRCfePsBMrt2UFEN/YIvyxzOZA/PDDs504Xi4FpjcvZx8WxUX8YHz/mlFQG8M55/jH3sr6fu/YfFEYybcVAHexDxW7XviMwxfbhAX/2oa727KmkOwr+8wWhfPahEWducQCdbdn9txX8SHh7l28/esJ//mCgy/fdBqcvvdyws784JdF1ZhciEiR2ISJBYhciEiR2ISJBYhciEiR2ISJBYhciEuoeZ/fmtS63BfKTR5xjE/148tAxPy7KQN63OSV2Oexveyf8WPXXu1/t2t807wnX/psnZZeE3jHib3vYq4MNYFHnQE32nDNf//Mjfox+7/NzXfv4UPac9ABwwsvt7vDj6C2t/vwHudD+4sS7AQAF53mTMf8czEC+exY6swsRCRK7EJEgsQsRCRK7EJEgsQsRCRK7EJEgsQsRCcE4O8nlAL4K4GQkM1pvMLMvklwP4I8AHEpXvdbM7vI7M1iHE78MxBfLXU7bUFzTiZMDfhw92D7U9qgfy/7ZzpWuvb/oPyOwpPN4pm0skM9eyPnx5J7CqGs/MNzj2vcPZNuPH8+e7x4A7Jg/boUhf3/Jj2R/L0Y/Rm8tfhw9FOrOdQTi8E6NhOC2vd3BGZJqHqopAbjazDaT7AGwieTdqe0LZvbZKvoQQjSYoNjNbB+AfenrAZKPA1g6244JIWaWl/SbneRKAK8E8FC66KMkHyF5A8l5GW3WkdxIcuP44FBNzgohpk/VYifZDeA2AB83s34A/wBgNYA1SM78n5uqnZltMLO1ZrY23+0/Cy2EmD2qEjvJAhKh32xmtwOAmR0ws3EzKwP4MoDzZ89NIUStBMVOkgCuB/C4mX2+YnlfxWrvBvDYzLsnhJgpqrkb/1oAlwN4lOSWdNm1AC4juQZJkdgdAD5cqzPs9NMOzQnN5ZySykA45ZBOKiYAlEeyhyqUchiyjx3xp7neWu5z7Tu7prxdAgBY0O3fJ5nbOuzaS4HQ3dERPyw4OJT92cpjft8MnIoCla6Rc6KGgYhjOJQbOk36uxOKnc5O0eY753btpN5Wczf+J5g6C92PqQshmgo9QSdEJEjsQkSCxC5EJEjsQkSCxC5EJEjsQkRC3aeSZkt2fNGLowOBtoG4aEvBj12WAjFfDwscMnPFQIw/8LlLA3465qBj62rLLpkMAAvbvdZAZ4vf/siIn6ba050dxx8MTMdcHPWfPwidqnKBOLxH6NmIQBg9uE/A2ZcZCPGfeuWmTNtRO5Fp05ldiEiQ2IWIBIldiEiQ2IWIBIldiEiQ2IWIBIldiEigWShiOIMbIw8B2FmxaAGAw3Vz4KXRrL41q1+AfJsuM+nbCjNbOJWhrmJ/0cbJjWa2tmEOODSrb83qFyDfpku9fNNlvBCRILELEQmNFvuGBm/fo1l9a1a/APk2XeriW0N/swsh6kejz+xCiDohsQsRCQ0RO8mLSD5JchvJTzXChyxI7iD5KMktJDc22JcbSB4k+VjFsl6Sd5N8Ov2fPWl8/X1bT3JPOnZbSF7cIN+Wk/z/JB8nuZXkn6bLGzp2jl91Gbe6/2YnmQfwFIC3ANgN4GEAl5nZv9bVkQxI7gCw1swa/gAGydcjmZviq2Z2drrsfwA4YmbXpQfKeWb2ySbxbT2AwUaX8U6rFfVVlhkH8C4AV6KBY+f4dSnqMG6NOLOfD2CbmW03syKAWwFc0gA/mh4zewDAkUmLLwFwU/r6JiQ7S93J8K0pMLN9ZrY5fT0AYKLMeEPHzvGrLjRC7EsB7Kp4vxvNVe/dAPyY5CaS6xrtzBQsNrN9QLLzAFjUYH8mEyzjXU8mlRlvmrGbTvnzWmmE2KeaYauZ4n+vNbNXAfgdAB9JL1dFdVRVxrteTFFmvCmYbvnzWmmE2HcDWF7xfhmAvQ3wY0rMbG/6/yCAO9B8pagPTFTQTf8fbLA/L9BMZbynKjOOJhi7RpY/b4TYHwZwKslTSLYCeD+AOxvgx4sg2ZXeOAHJLgBvRfOVor4TwBXp6ysAfLeBvvwKzVLGO6vMOBo8dg0vf25mdf8DcDGSO/LPAPjzRviQ4dcqAL9M/7Y22jcAtyC5rBtDckX0IQDzAdwD4On0f28T+fY1AI8CeASJsPoa5NvrkPw0fATAlvTv4kaPneNXXcZNj8sKEQl6gk6ISJDYhYgEiV2ISJDYhYgEiV2ISJDYhYgEiV2ISPg3245sv2HgEhgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for output_idx in np.arange(10):\n",
    "    # Lets turn off verbose output this time to avoid clutter and just see the output.\n",
    "    img = visualize_activation(model, layer_idx, filter_indices=output_idx, input_range=(0., 1.))\n",
    "    plt.figure()\n",
    "    plt.title('Networks perception of {}'.format(output_idx))\n",
    "    plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty cool. Its amazing that we can even generate an input image via backprop! \n",
    "\n",
    "Obviously you can tune the visualizations to look better by experimenting with `image_modifiers`, lp-norm weight etc. Basically, a regularizer is needed to enforce image naturalness prior to limit the input image search space. By this point, GANs should come to your mind. We could easily take a GAN trained on mnist and use discriminator loss as a regularizer. For using custom loss, you can use `visualize_activation_with_losses` API.\n",
    "\n",
    "Feel free to submit a PR if you try the GAN regularizer :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other fun stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The API to `visualize_activation` accepts `filter_indices`. This is generally meant for *multi label* classifiers, but nothing prevents us from having some fun. \n",
    "\n",
    "By setting `filter_indices=[1, 7]`, we can generate an input that the network thinks is both 1 and 7 simultaneously. Its like asking the network\n",
    "\n",
    "> Generate input image that you think is both 1 and a 7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e1effc1eb8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = visualize_activation(model, layer_idx, filter_indices=[1, 7], input_range=(0., 1.))\n",
    "plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare this to the `1` generated above and you should be able to see the difference. Nifty indded!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizations without swapping softmax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As alluded at the beginning of the tutorial, we want to compare and see what happens if we didnt swap out softmax for linear activation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LRT09k4o66j8Xo8+NL3j/AXFh3rZjDgvt/vATSZu1x+eanD1XCnt026ik7d8mplOa10nPw+/PuqPS5EA4huDUcfFc+O8tTZcn/0Hbi0mbzuxCFILELkQhSOxCFILELkQhSOxCFILELkQhSOxCFEJT4+xLF+8W5l/P5SAnKP+bi6PP64rzhD8X5DeHOL96W8cBYVuIY/i5POE3rYzb9/jASxPn8sb7tm2hvbbosdA+d9V9r9qnXnLzunNFkX/14ui0MTM2IlfuORdH78mUbJ7akR634d3xsfi349+etK3o+WXSlj2zm9llZrbWzB5qWLanmd1iZk9U/8fk+hFCtJb+XMZfDpy63bLPA7e6+8HArdV7IcQwJit2d78TWL/d4unAFdXrK4AzhtgvIcQQM9AHdPu6+xqA6v8+qQ+a2YVmttDMFnb7lgGuTggxWHb403h3n+3uk9198khLT0wQQuxYBir2Z8xsP4Dq/9qhc0kIsSMYqNjnA+dXr88H5g2NO0KIHUU2zm5mVwNTgL3NbDVwMfA14Boz+ziwEogLq7/SW5grPBdv/tHyO5K2D2Xyo7/kcbx4TNvOoT2aW33DHZ1h21Mz38uDvO8Aa7ZtDu17t++StOVi1bmxDadn6ozbiLi2/FmHvytp85cyz3AysWqvxTnrozEE312enhMO0DFi19Cey5efJfpumX02d2V6n538vueTtqzY3f28hCm9F4UQww4NlxWiECR2IQpBYheiECR2IQpBYheiEJo6xXXi0Zu49sY7k/aZ408O2+fCaxGjM6P3pmbS93Z2pf2OyuQC/PDJOMzz0XEnhfYotAbQFuQlnn7UKWHb2uZ0qAbAdspMnw2mHQP84rH0dz/zyHfHXT+3MV53puRzNK25x4NS0sDG2kuhfde2OOQ4ymJ7Z9evk7azDpmS6Tst22hv6cwuRCFI7EIUgsQuRCFI7EIUgsQuRCFI7EIUgsQuRCE0Nc5uWFjqdss7jwnb7/Kbx5O2uY/emll7JmVyZrrkmQemp6nevPr+sG0uTn5Dpv22TNLktuC7ZWPVmWmkuVh2LuVylOLbe+Kpu2Smkc5dFU/PjbYLFn/vUTtYGru2peP885+4K2y7xdNjG6ItpjO7EIUgsQtRCBK7EIUgsQtRCBK7EIUgsQtRCBK7EIXQ1Dh7jlG3LQrtPdvSKZdfqMXpmPfIlOi9blVcFnnlthfTfnmcdriWiZMPNi1xD+k02VHaYSAsoQ3gmfnqQcgXgKiy8fxV94Ztc+m/Z3TEeQDCOeNTz0/aAPzhJ0J7trx4hi2ePl5PH5su5wzw4xXp3Aq1INKuM7sQhSCxC1EIErsQhSCxC1EIErsQhSCxC1EIErsQhTCs4uzZudVBPPq8CVPCpjetiOPocRQeJo4cnbR1Z4LNIzK9R3FygBnjTwztcX70TFnjow4J7b5oSdw+M589IprrDtC5/O7QnstBEI0huHr5pWHbKDc7ZObKkz8morwOubERm4OvPaj57GZ2mZmtNbOHGpbNMrOnzOzB6u+0XD9CiNbSn8v4y4FT+1j+LXefVP3dMLRuCSGGmqzY3f1OYH0TfBFC7EAG84DuU2a2qLrMH5P6kJldaGYLzWzhumczA6mFEDuMgYr9e8BEYBKwBvhG6oPuPtvdJ7v75L33yj0GE0LsKAYkdnd/xt173L0GfB+IS6AKIVrOgMRuZvs1vD0TeCj1WSHE8CAbZzezq4EpwN5mthq4GJhiZpOoh/W6gL/pz8qWLtotzL+enxydjul+Z2lcAz0/53zgc8qj+uj9IdwmQNvOAx8OMfOUD4X22mNxHD079iE7QmHgfee2Sz7nfbpGendmf38gs+5cLHzptti3Q0emt1t7cJwDfOykv07aulb/KGnLHkXufl4fi+MRCUKIYYeGywpRCBK7EIUgsQtRCBK7EIUgsQtRCM2d4mrxlMjOrvvC5mcdMiVp6xgRh9a2ZNISZ8M8AbmUyDlyYZxsaC74za49uWJAPr1MpmTzj5bfEdrHtKXLVW8jDrVOH5fbJ5mwX3Cs7RrluO4HuX1i7QP3bcGydApsgNradWnjtvRxrjO7EIUgsQtRCBK7EIUgsQtRCBK7EIUgsQtRCBK7EIXQ1Dj7QUc/z7yb0jHl6R1xymSClMtTO+Iyt7npkJlwcpi2OJcS+fquOA4/vSPO/TF3ZRx3jeLN7fu/LmzZefe1mb5jRpCOo0M8viFXLtqOPDi01xY/Htrnr0inou7xeIfnxj5M+cMHQ/tdk34S2qdPSB/ruRLfvjVd7tmD1OE6swtRCBK7EIUgsQtRCBK7EIUgsQtRCBK7EIUgsQtRCE0v2RyVEM6V6I3ijzMzMfpced/BzEnPlUXelks1nQnyd2fmfc8Yl45XX79ibth20iWfCe0HfG9xaPetW2N7d5RHIJO+++EnYvsOJDcGYE+L8wT0LM98t0GM23j8m29I2rb8651Jm87sQhSCxC5EIUjsQhSCxC5EIUjsQhSCxC5EIUjsQhRCf0o2jwWuBF4H1IDZ7n6Jme0J/AzooF62+Rx3//OOcxVmjj85sMZzgC/vivObT+94Z2if13VPaI/7juOmC7riudPTOt4e2uetSPsWZ8uH3Vdm5k6/+GJsz4xfoBaMEWiLc6svWBHXEZg27s2hPRr/MGN8JndCZmhELkfBloFXAMd2SpeaBjj0H/+QtP35pReStv6c2bcBF7n74cAJwCfN7Ajg88Ct7n4wcGv1XggxTMmK3d3XuPsD1etNwKPA64HpwBXVx64AzthRTgohBs+rumc3sw7gDcB9wL7uvgbqPwjAPkPtnBBi6Oi32M1sNDAH+Ky7b3wV7S40s4VmtnDds/EYbyHEjqNfYjezkdSF/mN376wWP2Nm+1X2/YC1fbV199nuPtndJ++91+CK6QkhBk5W7GZmwKXAo+7+zQbTfOD86vX5wLyhd08IMVRYlHoWwMxOBO4CFvNKfOsL1O/brwEOBFYCZ7v7+qivPdr29BNGvHfAzl69PD1977wwLDd42vdNP5LoeabPi5qXmbciDq1NX5J5tvneP8X9B2HBXNgvN604R7bUdZDCe+6qOLQ2Y+JJcdeZ6bXR1OGodDjk91luWnM2tBesv33Ma+J1P5uOcN/bfRMba8/22Xk2zu7ud5OOOr4r114IMTzQCDohCkFiF6IQJHYhCkFiF6IQJHYhCkFiF6IQml6yufPGXPnhNDM60rH0v3/0kbDtu3fZEPediYv2rF2XNmZSQb9QS5fYBeB9Qd/kp9eecXB6Cmzn8tvjdWfIxdFzpY1zKZlDejLDq3NjRNoz81QDuj1e91kHxuMXvrw0ngL78as+lbR1fHVh2DaK0YfNBtRKCPH/HRK7EIUgsQtRCBK7EIUgsQtRCBK7EIUgsQtRCE2Nsz+5ZAwzp5ybtF97+8/C9lHa4lwcfbA8Py1dJve2//xu2LZGnBo4G0/OMOfx25O2kRZnB5rWkYujx/Pdc3H0a7vS7c/MxKozwxf4fsa3lzwdj35tJlbdNkhpfOXQE0J7B0EsPcgBAGCW9i36VjqzC1EIErsQhSCxC1EIErsQhSCxC1EIErsQhSCxC1EI2bzxQ8kebXv5CaPeN+D21z6ZLrt81tg4Xmztcbw5W3o4YO3cg0P7zcddGtoXbJ4Y2n8+aVzsQOB727jXh01zYxtyDCaOn8tZn4vh50o6twVR56kTMvnuc8dDJhaeJRhEkNsuK7alC3Gf8/4/8fCirX1+cZ3ZhSgEiV2IQpDYhSgEiV2IQpDYhSgEiV2IQpDYhSiE7KRdMxsLXAm8jnp99tnufomZzQI+AfQWD/+Cu98Q9jVqJ9omHJi0f+PGK0JfZk58R9I2d9VdYdtczHbOyjg3+znv+XDStu/MZWHbj3g6r3u/sIHHfP2ZOCd9jThenMuPnqtzfv2KdP70qR1xrn6vxfP8c753B2NI7PB4bMP8668K7bk8ADmiWgA9md195pUXJW0rn/1W0tafGfrbgIvc/QEz2x2438xuqWzfcvev96MPIUSLyYrd3dcAa6rXm8zsUSAeliWEGHa8qnt2M+sA3gD0jlP8lJktMrPLzGxMos2FZrbQzBZu7XlhUM4KIQZOv8VuZqOBOcBn3X0j8D1gIjCJ+pn/G321c/fZ7j7Z3Sfv1L7rELgshBgI/RK7mY2kLvQfu3sngLs/4+497l4Dvg8cv+PcFEIMlqzYzcyAS4FH3f2bDcv3a/jYmcBDQ++eEGKo6M/T+LcBHwYWm9mD1bIvAOeZ2STAgS7gb7I9bdmKL1+VNI8bkXEnmHbYbpkytpm8xDMzYaAFK34a9x8wbdybQ3tueu38VXFYsCcIMeVKUc8cny6DXfUeWmc9Hk8z3RaExzbOOC5su0fnA6F9MNQWLQntuZLN2em5me0+vSMIaWbCmb964t+TttOuSYda+/M0/m76TkcdxtSFEMMLjaATohAkdiEKQWIXohAkdiEKQWIXohAkdiEKoamppP9q5D7+lr3PTjuTibN3/nZe0nbGIVPCtnOW/Cq0H3nL34X2wz71WNL28yW3hm3POeq9of2ah24O7Rtq6dTBAKc98Imk7deTfxi2zaWCfqHWHdrPmzAltIcx41yp6lzN5hzB1N8fLrs9bPre//hcaN//O/fH686log62S04H6886Jml75Lpv8fy6VUolLUTJSOxCFILELkQhSOxCFILELkQhSOxCFILELkQhNDXObmZ/AlY0LNobiHMdt47h6ttw9Qvk20AZSt/Guftr+zI0Vex/sXKzhe4+uWUOBAxX34arXyDfBkqzfNNlvBCFILELUQitFvvsFq8/Yrj6Nlz9Avk2UJriW0vv2YUQzaPVZ3YhRJOQ2IUohJaI3cxONbMlZrbUzD7fCh9SmFmXmS02swfNbGGLfbnMzNaa2UMNy/Y0s1vM7Inqf5819lrk2ywze6radg+a2Wkt8m2smf3KzB41s4fN7DPV8pZuu8Cvpmy3pt+zm1k78DjwHmA18DvgPHd/pKmOJDCzLmCyu7d8AIaZnQxsBq5096OqZf8GrHf3r1U/lGPc/Z+GiW+zgM2tLuNdVSvar7HMOHAG8FFauO0Cv86hCdutFWf244Gl7r7M3bcCPwWmt8CPYY+73wms327xdOCK6vUV1A+WppPwbVjg7mvc/YHq9Sagt8x4S7dd4FdTaIXYXw801oBazfCq9+7AL83sfjO7sNXO9MG+7r4G6gcPsE+L/dmebBnvZrJdmfFhs+0GUv58sLRC7H3lxxpO8b+3uftxwPuAT1aXq6J/9KuMd7Poo8z4sGCg5c8HSyvEvhoY2/D+AODpFvjRJ+7+dPV/LTCX4VeK+pneCrrV/7Ut9udlhlMZ777KjDMMtl0ry5+3Quy/Aw42s/FmthPwAWB+C/z4C8xst+rBCWa2G3AKw68U9Xzg/Or1+UA65W6TGS5lvFNlxmnxtmt5+XN3b/ofcBr1J/JPAl9shQ8JvyYAf6j+Hm61b8DV1C/ruqlfEX0c2Au4FXii+r/nMPLtKmAxsIi6sPZrkW8nUr81XAQ8WP2d1uptF/jVlO2m4bJCFIJG0AlRCBK7EIUgsQtRCBK7EIUgsQtRCBK7EIUgsQtRCP8PMASQq3cn7zsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Swap linear back with softmax\n",
    "model.layers[layer_idx].activation = activations.softmax\n",
    "model = utils.apply_modifications(model)\n",
    "\n",
    "for output_idx in np.arange(10):\n",
    "    # Lets turn off verbose output this time to avoid clutter and just see the output.\n",
    "    img = visualize_activation(model, layer_idx, filter_indices=output_idx, input_range=(0., 1.))\n",
    "    plt.figure()\n",
    "    plt.title('Networks perception of {}'.format(output_idx))\n",
    "    plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not work! The reason is that maximizing an output node can be done by minimizing other outputs. Softmax is weird that way. It is the only activation that depends on other node output(s) in the layer."
   ]
  }
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